Modeling of Chaotic Behavior of Benchmark Datasets using Hybrid Heuristic Optimization

Abstract

Optimization is required for producing the best results. Heuristic algorithm is one of the techniques which can be used for finding best results. By making use of artificial neural network and particle swarm optimization values can be predicted and chaotic signals can be modeled which forms the base of this project. The chaotic signals here use are Mackey series and Box Jenkins Gas Furnace data series. The results of this work shows the comparative study of predicted number of neurons in the second hidden layer also it gives the value of mean square error while making the prediction.